library(data.table)
library(ggplot2)
library(knitr)
library(ggrepel)
library(RColorBrewer)
library(DESeq2)
#library(rnaseqGene)
library(plotly)
library(Rtsne)
library(scales)
library(dplyr)
rm(list = ls())
setDTthreads(8)
data.output.dir <- file.path(here::here(
'..','..',
's3-roybal-tcsl',
'lenti_screen_compiled_data','data'))
load(file=file.path(data.output.dir, 'pooled_analysis_data.Rdata'))
# set this to true to re-run, else will load from s3
rerun_deseq <- F
# arrayed list of CARs
array.list <- c('BAFF-R','TNR8','4-1BB','TACI','CD28','KLRG1','CD40')
# load colors
source(here::here('r','fig_colors.R'))
We’d like to use DESeq2’s normalization for CAR-score and to properly normalize the baseline measurements.
This can be done with either VST or rlog.
https://www.biostars.org/p/188808/ https://support.bioconductor.org/p/126713/
cts <- dcast(
read.counts[, .(
CAR.align,
sort.group.bin,
k.type, t.type, batch, assay, donor,
sort.group,
bin,
counts)],
CAR.align ~ sort.group.bin, value.var='counts')
cts <- data.frame(cts[, -1], row.names = cts[, CAR.align])
cts[is.na(cts)] <- 0
vst_cts <- reshape2::melt(varianceStabilizingTransformation(as.matrix(cts)))
## converting counts to integer mode
names(vst_cts) <- c('CAR.align','sort.group.bin', 'vst')
rlog_cts <- reshape2::melt(rlog(as.matrix(cts)))
## rlog() may take a long time with 50 or more samples,
## vst() is a much faster transformation
## converting counts to integer mode
names(rlog_cts) <- c('CAR.align','sort.group.bin', 'rlog')
rlog_cts$CAR.align <- factor(rlog_cts$CAR.align, levels=1:40,
labels=levels(vst_cts$CAR.align))
norm_cts <- merge(
vst_cts, rlog_cts, by=c('CAR.align','sort.group.bin'))
# convert 'post.cytof' to 'post-cytof'
levels(norm_cts$sort.group.bin) <- gsub(
'\\.','-',levels(norm_cts$sort.group.bin))
read.counts <- merge(
read.counts, norm_cts, by=c('CAR.align','sort.group.bin'), all=T)
read.counts <- read.counts[!is.na(t.type)]
#prolif 1 & 2
read.counts[,
vst_car_score := sum((vst - mean(vst)) * sqrt(ctv.bin.score)),
by=c('CAR.align','sort.group')]
read.counts[,
rlog_car_score := sum((rlog - mean(rlog)) * sqrt(ctv.bin.score)),
by=c('CAR.align','sort.group')]
## post-cytof
read.counts[batch=='post-cytof',
vst_car_score := sum((vst - mean(vst)) * sqrt(bin.score)),
by=c('CAR.align','sort.group')]
read.counts[batch=='post-cytof',
rlog_car_score := sum((rlog - mean(rlog)) * sqrt(bin.score)),
by=c('CAR.align','sort.group')]
# save an uneditied copy of read.counts for cytokines later
read.counts.complete <- copy(read.counts)
ggplot(read.counts) +
geom_boxplot(
aes(x=reorder(CAR.align,vst_car_score), y=vst_car_score)) +
coord_flip() +
facet_wrap(t.type ~ k.type) +
facet_grid(t.type ~ assay + k.type)
ggplot(read.counts) +
geom_boxplot(
aes(x=reorder(CAR.align,rlog_car_score), y=rlog_car_score)) +
coord_flip() +
facet_wrap(t.type ~ k.type) +
facet_grid(t.type ~ assay + k.type)
ggplot(read.counts) +
geom_point(
aes(x=rlog_car_score, y=CAR.score, color=interaction(donor, batch))) +
coord_flip() +
facet_wrap(t.type ~ k.type) +
facet_grid(t.type ~ assay + k.type)
ggplot(read.counts) +
geom_point(
aes(x=rlog_car_score, y=vst_car_score, color=interaction(donor, batch))) +
coord_flip() +
facet_wrap(t.type ~ k.type) +
facet_grid(t.type ~ assay + k.type)
subset_barplot_reads <- read.counts[
assay == 'CTV1' & batch == 'prolif2' & donor == 'd1' & k.type == 'pos']
subset_barplot_reads <- subset_barplot_reads[,
CAR.ranked.sep := factor(
paste(CAR.align,t.type,sep='|'))]
subset_barplot_reads[,
CAR.ranked.sep := factor(CAR.ranked.sep, levels=levels(
reorder(
CAR.ranked.sep[k.type == 'pos'],
-rlog_car_score[k.type == 'pos'])))]
remove_t_type_sep <- function (breaks)
unlist(lapply(breaks, function(str)
strsplit(str, '|',fixed=T)[[1]][1]))
barplot_stacked_plot <- ggplot(subset_barplot_reads) +
geom_bar(aes(y=car.bin.pct, x=CAR.ranked.sep, fill=bin),
stat='identity', width=1) +
scale_fill_brewer(palette = "YlGn") +
facet_wrap(. ~ t.type, scales = 'free_x', ncol=1) +
labs(y='% sorted into each bin', x='CAR Costimulatory Domains, ranked') +
scale_x_discrete(expand = c(0, 0), labels=remove_t_type_sep) +
scale_y_continuous(limits = c(0,1), expand = expansion(mult = c(0, 0)),
breaks=c(0, .5, 1), labels=percent) +
theme_bw(base_size=18) + theme(axis.ticks = element_blank()) +
theme(
axis.text.x = element_text(size=9, angle = 90, vjust = 0.5, hjust=1),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
strip.background = element_blank(),
panel.border = element_rect(colour = "black"))
ggsave(here::here('..','figs','pooled','fig1_stacked_bar.pdf'),
barplot_stacked_plot, height=3, width=9, useDingbats=FALSE)
run_deseq <- function(data.dt, ref_bin, test_bin,
control_replicates = T,
interaction = T, group.control = F, weight.bins = F)
{
# identify inputs
assay_input <- as.character(unique(data.dt[, assay]))
k_type <- as.character(unique(data.dt[, k.type]))
t_type <- as.character(unique(data.dt[, t.type]))
## 1. Do bin normalization weights =======
# bin normalization weights
data.weights <- unique(data.dt[, list(
batch, donor, timepoint, assay, t.type, k.type,
sort.group, bin, bin.pct, bin.reads)])[,
list(bin, bin.reads, bin.pct, sort.group,
read.weight=bin.pct * bin.reads / sum(bin.pct * bin.reads)),
by=.(batch, donor, timepoint, assay, t.type, k.type)][,
read.weight.norm := read.weight/exp(mean(log(read.weight))),
by=.(batch, donor, timepoint, assay, t.type, k.type)]
stopifnot(nrow(interaction(assay_input, k_type, t_type)) == 1)
# prepare cts and coldata dataframes
## 2. Prepare Ref Bins ============
if(length(ref_bin) == 1 & ref_bin[1] == 'baseline') {
# reference is baseline
# get baseline counts per donor/assay replicate
ref.bin.dt <- dcast(
data.dt[
bin == 'D' & assay == assay_input &
k.type == k_type & t.type == t_type,
.(
CAR.align,
bin.sort.group = paste(
batch, donor, timepoint, assay, t.type, 'base', sep = '_'),
k.type, t.type, batch, assay, donor,
sort.group,
bin = 'base',
counts = baseline.counts)],
CAR.align ~ bin.sort.group, value.var='counts')
ref.weights <- rep(1, ncol(ref.bin.dt))
} else {
# reference is a specified bin
ref.bin.dt <- dcast(
data.dt[
bin %in% ref_bin & assay == assay_input &
k.type == k_type & t.type == t_type,
.(
CAR.align,
bin.sort.group = paste(sort.group, bin, sep = '_'),
k.type, t.type, batch, assay, donor,
sort.group,
bin,
counts)],
CAR.align ~ bin.sort.group, value.var='counts')
ref.weights <- dcast(data.weights[
bin %in% ref_bin & assay == assay_input &
k.type == k_type & t.type == t_type,
.(
bin.sort.group = paste(sort.group, bin, sep = '_'),
k.type, t.type, batch, assay, donor,
sort.group,
bin,
read.weight.norm)],
. ~ bin.sort.group,
value.var = 'read.weight.norm')
stopifnot(nrow(ref.bin.dt) == nrow(unique(ref.bin.dt)))
}
# copy the ref bin columns for each of the test bin columns
if (interaction == T) {
num.ref.reps <- ncol(ref.bin.dt) - 1
ref.bin.dt <- cbind(ref.bin.dt[, 1],
do.call("cbind", replicate(length(test_bin),
ref.bin.dt[, -1], simplify = FALSE)))
names(ref.bin.dt) <- c(names(ref.bin.dt[, 1]),
paste(names(ref.bin.dt[, -1]), rep(test_bin, each=num.ref.reps),
sep = '_'))
}
## 3. Prepare Test Bins ============
test.bin.dt <- dcast(
data.dt[
bin %in% test_bin & assay == assay_input &
k.type == k_type & t.type == t_type,
.(
CAR.align,
bin.sort.group = paste(sort.group, bin, sep = '_'),
k.type, t.type, batch, assay, donor,
sort.group,
bin,
counts)],
CAR.align ~ bin.sort.group, value.var='counts')
# check that replicate counts match
stopifnot(nrow(ref.bin.dt) == nrow(unique(ref.bin.dt)))
stopifnot(nrow(test.bin.dt) == nrow(unique(test.bin.dt)))
## 4. Merge and create design matrix ============
cts <- merge(ref.bin.dt, test.bin.dt, by = 'CAR.align')
cts <- data.frame(cts[, -1], row.names = cts[, CAR.align])
cts[is.na(cts)] <- 0
coldata <- data.frame(
condition = c(
rep('reference', ncol(ref.bin.dt) - 1),
rep('test', ncol(test.bin.dt) - 1)),
rep = data.table(t(sapply(strsplit(c(
names(ref.bin.dt)[-1],
names(test.bin.dt)[-1]),"_"), `[`, c(1,2))))[,
paste(V1, V2, sep='_')],
bin = sapply(strsplit(c(
names(ref.bin.dt)[-1],
names(test.bin.dt)[-1]),"_"), `[`, 7),
row.names = c(names(ref.bin.dt)[-1], names(test.bin.dt)[-1]))
dds <- DESeqDataSetFromMatrix(countData = cts,
colData = coldata,
design = ~ condition + rep)
# set reference
dds$condition <- relevel(dds$condition, ref = "reference")
print(coldata)
# pre-filtering
keep <- rowSums(counts(dds)) >= 10
dds <- dds[keep,]
# try various designs of decreasing complexity, since we don't have a
# full matrix for every example deseq run.
# try_designs <- function(designs, dds) tryCatch({
# message(paste("trying design:",as.character(designs[[1]]),"\n"))
# DESeq2::design(dds) <- designs[[1]]
# return(DESeq2::DESeq(dds)) }, error= function(e) {
# dds <- try_designs(designs[c(-1)], dds)
# message(e)
# message('getting here\n')
# return(dds)
# }
# )
#
# try_designs(
# designs=c(
# ~ condition + condition:rep + condition:bin,
# ~ condition + rep + bin,
# ~ condition + rep,
# ~ condition
# ),
# dds
# )
# if (control_replicates) {
# design(dds) <- ~ condition + rep
# }
#
# #check unique bins before using bins as contrast
# n_uniq_bins <- length(unique(coldata$bin))
#
# if (n_uniq_bins == 1 & interaction == T) {
# warning('Cannot use bin contrast, only one bin level.')
# }
#
# if (interaction == T & group.control == T & n_uniq_bins > 1) {
# design(dds) <- ~ condition + condition:rep + condition:bin
# } else if (interaction == T & n_uniq_bins > 1) {
# design(dds) <- ~ condition + rep + bin
# }
dds <- DESeq2::DESeq(dds)
res <- results(dds)
# shrink log fold change
resLFC <- lfcShrink(dds,
coef="condition_test_vs_reference", type="apeglm")
# convert to data.table
results.dt <- as.data.table(resLFC)[, CAR.align := row.names(resLFC)]
results.dt <- cbind(results.dt[, 6], results.dt[, -6])
results.dt[, assay := assay_input][, k.type := k_type][, t.type := t_type]
return(results.dt)
}
if (rerun_deseq) {
test_ref_sets <- c(ref=list(), test=list())
# A/B/AB vs C/D/CD
test_ref_sets$ref <- c(as.list(rep('D',3)), as.list(rep('C',3)), rep(list(c('C','D')),3))
test_ref_sets$test <- rep(list('A','B',c('A','B')),3)
test_ref_sets$interaction <- as.list(rep(F, 9))
# A/B/AB/ABCD vs baseline
test_ref_sets$ref <- c(test_ref_sets$ref, as.list(rep('baseline',3)))
test_ref_sets$test <- c(test_ref_sets$test, list('A','B',c('A','B','C','D')))
test_ref_sets$interaction <- c(test_ref_sets$interaction, as.list(rep(T, 3)))
all.deseq.results.dt <- data.table()
for (set_i in seq_along(test_ref_sets$ref)) {
ref_set <- test_ref_sets$ref[[set_i]]
test_set <- test_ref_sets$test[[set_i]]
ref_str <- paste0(ref_set, collapse='')
test_str <- paste0(test_set, collapse='')
inter <- test_ref_sets$interaction[[set_i]]
deseq.results.dt <- read.counts[batch != 'post-cytof' & !is.na(k.type),
{
message(paste(c(ref_str, test_str, inter, .BY[1],"\n"), collapse= ' - '));
tryCatch(
run_deseq(
data.dt = .SD,
ref_bin = ref_set,
test_bin = test_set,
interaction = inter,
group.control = T),
error= function(e) {message(e); return(data.table())}
)
},
by = .(group)]
if (nrow(deseq.results.dt) > 0) {
deseq.results.dt[,
`:=`(
ref_set = ref_str,
test_set = test_str,
inter = inter)]
}
all.deseq.results.dt <- rbind(
all.deseq.results.dt,
deseq.results.dt, fill=T)
}
save(list=c('all.deseq.results.dt'),
file=file.path(data.output.dir, 'pooled_deseq2_data.Rdata'))
}
if (!rerun_deseq) load(
file=file.path(data.output.dir, 'pooled_deseq2_data.Rdata'))
#add back CAR scores
cols_to_add <- c('CAR.score','sort.group','donor','batch')
cols_to_join <- c('group', 'CAR.align', 'assay', 'k.type', 't.type')
all.deseq.results.dt[, padj.disp := -log10(padj)]
all.deseq.results.dt[, lfc.disp := log2FoldChange]
all.deseq.results.dt[padj.disp > 10, padj.disp := Inf]
all.deseq.results.dt[abs(lfc.disp) > 5, lfc.disp := sign(lfc.disp) * Inf]
# mask receptor names except for known ones
control_domains <- c('4-1BB','CD28')
chosen_domains <- c('BAFF-R','CD40','TACI','TNR8')
neg_domain <- c('KLRG1')
all.deseq.results.dt[, CAR.type := 'other']
all.deseq.results.dt[CAR.align %in% control_domains, CAR.type := 'control']
all.deseq.results.dt[CAR.align %in% chosen_domains, CAR.type := 'chosen']
all.deseq.results.dt[CAR.align %in% neg_domain, CAR.type := 'neg']
all.deseq.results.dt[,
CAR.type := factor(CAR.type,levels=c('other','control','chosen','neg'))]
make_volcanoes <- function(data.dt) {
ggplot(data.dt, aes(
x=lfc.disp, y=padj.disp,
color=CAR.type,
label=CAR.align,
size=CAR.type)) +
geom_point() +
geom_hline(yintercept=-log10(0.05), linetype=2) +
facet_grid(test_set + ref_set ~ t.type + assay + k.type) +
scale_color_manual('',
labels=c('Other Receptors', 'CD28/4-1BB', 'New Receptors','Negative'),
values=c('grey50', RColorBrewer::brewer.pal(5, 'Paired')[c(2,4,5)])) +
scale_size_manual('',
labels=c('Other Receptors', 'CD28/4-1BB', 'New Receptors','Negative'),
values=c(1,3,3,3)) +
labs(x='Log2 FC', y='-log10(P-value)', title='Assay Volcano Plots')
}
make_timeseries <- function(data.dt) {
ggplot(data.dt, aes(
y=lfc.disp, x=assay,
color=CAR.type,
group=CAR.align,
label=CAR.align,
size=CAR.type)) +
geom_point() +
geom_line() +
facet_grid(t.type ~ test_set + ref_set) +
scale_color_manual('',
labels=c('Other Receptors', 'CD28/4-1BB', 'New Receptors','Negative'),
values=c('grey50', RColorBrewer::brewer.pal(5, 'Paired')[c(2,4,5)])) +
scale_size_manual('',
labels=c('Other Receptors', 'CD28/4-1BB', 'New Receptors','Negative'),
values=c(0.5,1,1,1)) +
labs(y='Log2 FC', x='Assay', title='Log fold change across assays')
}
make_cd4_cd8 <- function(data.dt) {
ggplot(
dcast(data.dt,
CAR.align + assay + k.type + ref_set + test_set + inter + CAR.type ~ t.type,
value.var = c("log2FoldChange", "padj.disp")),
aes(y=log2FoldChange_CD8, x=log2FoldChange_CD4,
color=CAR.type,
label=CAR.align,
size=CAR.type)) +
geom_point() +
facet_grid(test_set + ref_set ~ assay + k.type) +
scale_color_manual('',
labels=c('Other Receptors', 'CD28/4-1BB', 'New Receptors','Negative'),
values=c('grey50', RColorBrewer::brewer.pal(5, 'Paired')[c(2,4,5)])) +
scale_size_manual('',
labels=c('Other Receptors', 'CD28/4-1BB', 'New Receptors','Negative'),
values=c(1,3,3,3)) +
labs(x='CD4', y='CD8', title='Log fold change, CD4 vs CD8')
}
make_pos_neg <- function(data.dt) {
ggplot(
dcast(data.dt,
CAR.align + assay + t.type + ref_set + test_set + inter + CAR.type ~ k.type,
value.var = c("lfc.disp", "padj.disp")),
aes(y=lfc.disp_pos, x=lfc.disp_neg,
color=CAR.type,
label=CAR.align,
size=CAR.type)) +
geom_point() +
facet_grid(test_set + ref_set ~ t.type + assay) +
scale_color_manual('',
labels=c('Other Receptors', 'CD28/4-1BB', 'New Receptors','Negative'),
values=c('grey50', RColorBrewer::brewer.pal(5, 'Paired')[c(2,4,5)])) +
scale_size_manual('',
labels=c('Other Receptors', 'CD28/4-1BB', 'New Receptors','Negative'),
values=c(1,3,3,3)) +
labs(x='CD19-', y='CD19+', title='Log fold change, CD19+ vs CD19-')
}
Comparisons on x/y, all combos
x_group <- 'prolif2_d1_3d_CTV1_CD4_neg'
y_group <- 'prolif2_d1_3d_CTV1_CD4_pos'
cast_comparison <- function(
comp.df, x_group, y_group, value_col='CAR.score', xycols=c('x','y'),
rescale= 'combined') {
#message(paste(x_group, y_group, sep=', '))
cast_groups <- dcast(
unique(comp.df[sort.group %in% c(x_group, y_group),
list(CAR.align, sort.group, get(value_col))]),
CAR.align ~ sort.group, value.var = 'V3')[,
`:=`(x.group= x_group, y.group= y_group)]
names(cast_groups)[c(2,3)] <- xycols
stopifnot(rescale %in% c('combined','separate'))
# rescle == combined:
# rescale both x and y to (0,1) on same scale
xy_scalemin = cast_groups[, min(c(x,y), na.rm=T)]
xy_scalemax = cast_groups[, max(c(x,y), na.rm=T)]
tryCatch({
cast_groups[, x_scaled_comb := scales::rescale(
x, from=c(min(c(x,y), na.rm=T), max(c(x,y), na.rm=T)))]
cast_groups[, y_scaled_comb := scales::rescale(
y, from=c(min(c(x,y), na.rm=T), max(c(x,y), na.rm=T)))]
}, error = function(e) {
message(e)
cast_groups[, x_scaled_comb := NaN]
cast_groups[, y_scaled_comb := NaN]}
)
# rescle == separate:
# rescale x and y to (0,1) on individual scales
tryCatch({
cast_groups[, x_scaled_sep := scales::rescale(x)]
cast_groups[, y_scaled_sep := scales::rescale(y)]
}, error = function(e) {
message(e)
cast_groups[, x_scaled_sep := NaN]
cast_groups[, y_scaled_sep := NaN]}
)
}
# use malanhanobis distance to collapse replicates,
# then use median absolute deviation to identify outliers
#https://www.r-craft.org/r-news/combined-outlier-detection-with-dplyr-and-ruler/
maha_dist <- . %>% select_if(is.numeric) %>%
mahalanobis(center = colMeans(.), cov = cov(.))
isnt_out_maha <- function(tbl, isnt_out_f, ...) {
tbl %>% maha_dist() %>% isnt_out_f(...)
}
isnt_out_mad <- function(x, thres = 3, na.rm = TRUE) {
abs(x - median(x, na.rm = na.rm)) <= thres * mad(x, na.rm = na.rm)
}
top_x_mad <- function(x, top=5, na.rm = TRUE) {
mads <- abs(x - median(x, na.rm = na.rm))
top_mads <- order(-mads)[1:top]
return(!(1:length(mads) %in% top_mads))
}
plot_all_reps <- function(df=read.counts, value_col, df_only=F) {
all_rep_comparisons <- df[
grepl('CTV', assay)][order(assay)][order(t.type)][order(k.type)][,
data.table(matrix(combn(unique(sort.group), 2), ncol=2, byrow=T)),
by=c('assay','t.type','k.type')]
names(all_rep_comparisons)[4:5] <- c('x.group','y.group')
all_rep_comparisons <- all_rep_comparisons[,
cast_comparison(df, x.group, y.group, value_col=value_col)[,
c('x.group','y.group') := NULL],
by=c('assay','t.type','k.type','x.group','y.group')]
# combine with baseline abundance as color
baseline.abund <- df[
assay=='baseline', list(mean.baseline= mean(car.abund.baseline, na.rm=T)),
by=c('t.type','CAR.align')][,
list(CAR.align,
rel.baseline.log= log10(mean.baseline/mean(mean.baseline))),
by=c('t.type')]
all_rep_comparisons <-all_rep_comparisons[
baseline.abund, on=c('t.type','CAR.align')]
all_rep_comparisons[, rep_pair := paste(
gsub('(prolif\\d_d\\d).*','\\1', x.group),
gsub('(prolif\\d_d\\d).*','\\1', y.group),
sep='\n')]
all_rep_comparisons[
rep_pair == "prolif2_d2\nprolif1_d2", rep_pair := "prolif1_d2\nprolif2_d2"]
all_rep_comparisons[, assay_kt := paste(assay,t.type,k.type, sep='\n')]
# use malanhanobis distance to collapse replicates,
# then use median absolute deviation to identify outliers
all_rep_comparisons[,
outlier := {
nonsingular <- apply(tibble(x_scaled_sep, y_scaled_sep), 2,
function (x) var(x) > 0 & !is.na(var(x)))
non_singular_sd <- tibble(x_scaled_sep, y_scaled_sep)[,
(nonsingular)]
!isnt_out_maha(non_singular_sd, top_x_mad)
}, by=.(rep_pair, k.type, t.type, assay)]
all_rep_comparisons[, label_outliers := ''][outlier == T,
label_outliers := CAR.align]
all_rep_comparisons[, label_arrayed := ''][
CAR.align %in% array.list & !outlier,
label_arrayed := CAR.align]
non_na_comps <- all_rep_comparisons[,
!any(is.na(list(var(x_scaled_sep), var(y_scaled_sep)))),
by=.(rep_pair, k.type, t.type, assay)]
if (df_only) return(all_rep_comparisons)
r_squareds <- all_rep_comparisons[
non_na_comps[V1==T], on=.(rep_pair, k.type, t.type, assay)][V1 == T][,
list(r=sqrt(summary(lm(x_scaled_sep ~ y_scaled_sep))$r.squared)),
by=.(rep_pair, k.type, t.type, assay)]
r_squareds[, r_lbl := paste('r=',as.character(round(r, 2)))]
return(ggplot(data=all_rep_comparisons,
aes(x_scaled_sep, y_scaled_sep)) +
geom_point(shape = 21, colour = "grey30", aes(fill=rel.baseline.log)) +
geom_text_repel(size=2.5, color='grey20', aes(label=label_outliers)) +
geom_text_repel(size=2.5, color='lightsalmon4', aes(label=label_arrayed)) +
theme_bw() +
scale_fill_distiller(palette='BrBG',
limit=c(-1,1) * max(abs(all_rep_comparisons$rel.baseline.log))) +
geom_label(size=2.5, aes(x=Inf, y=-Inf, label=r_lbl), data=r_squareds,
hjust=1, vjust=0) +
facet_grid(rep_pair ~ k.type + t.type + assay))
}
read.counts[, car.abund.log := log10(car.abund)]
#assay_rep_list:
assay_rep_set <- unique(read.counts[
data.table(
assay=c('baseline','CTV1','CTV2','CTV3'),
prev_assay=c(NA,'baseline','CTV1','CTV2')), on='assay'][,
list(assay, sort.group, prev_assay, donor, batch, t.type, k.type)])
#map day0
assay_rep_set <- rbind(
assay_rep_set[assay=='baseline'][, k.type := 'pos'],
assay_rep_set[assay=='baseline'][, k.type := 'neg'],
assay_rep_set[assay!='baseline'])
#merge with prev copy to get prev sort.group correspondence
assay_rep_set <- assay_rep_set[, list(
donor, batch, t.type, k.type,
prev_assay=assay,prev.sort.group=sort.group)][
assay_rep_set,
on=c('donor','batch','t.type','k.type','prev_assay')]
# use baseline from prolif2 always
assay_rep_set[assay == 'CTV1',
prev.sort.group := gsub('prolif1','prolif2',prev.sort.group)]
#merge with prev measurements
prev_measure <- unique(read.counts[, list(
car.abund.prev=car.abund, prev.sort.group=sort.group, CAR.align)])[
assay_rep_set, on=c('prev.sort.group')]
#merge with orig read counts
read.counts <- prev_measure[, list(
car.abund.prev, prev.sort.group, CAR.align, sort.group)][
read.counts, on=c('sort.group','CAR.align')]
#calculate relative prev
read.counts[, car.abund.rel.prev := car.abund/car.abund.prev]
plot_all_reps(value_col='CAR.score') +
labs(title='CAR score replicate comparison')
## Warning in summary.lm(lm(x_scaled_sep ~ y_scaled_sep)): essentially perfect fit:
## summary may be unreliable
## Warning: Removed 1 rows containing missing values (geom_point).
## Warning: Removed 1 rows containing missing values (geom_text_repel).
## Warning: Removed 1 rows containing missing values (geom_text_repel).
plot_all_reps(value_col='car.abund') +
labs(title='CAR Abundance replicate comparison')
## Warning: Removed 1 rows containing missing values (geom_point).
## Warning: Removed 1 rows containing missing values (geom_text_repel).
## Warning: Removed 1 rows containing missing values (geom_text_repel).
plot_all_reps(value_col='car.abund.log') +
labs(title='CAR Log Abundance replicate comparison')
## Warning: Removed 1 rows containing missing values (geom_point).
## Warning: Removed 1 rows containing missing values (geom_text_repel).
## Warning: Removed 1 rows containing missing values (geom_text_repel).
plot_all_reps(value_col='car.abund.rel.baseline') +
labs(title='CAR Relative Abundance Change to Baseline, replicate comparison')
## Warning: Removed 1 rows containing missing values (geom_point).
## Warning: Removed 1 rows containing missing values (geom_text_repel).
## Warning: Removed 1 rows containing missing values (geom_text_repel).
plot_all_reps(value_col='car.abund.rel.prev') +
labs(title='CAR Relative Abundance Change to Previous, replicate comparison')
## Warning in min(x): no non-missing arguments to min; returning Inf
## Warning in max(x): no non-missing arguments to max; returning -Inf
## Warning in min(x): no non-missing arguments to min; returning Inf
## Warning in max(x): no non-missing arguments to max; returning -Inf
## Warning: Removed 81 rows containing missing values (geom_point).
## Warning: Removed 81 rows containing missing values (geom_text_repel).
## Warning: Removed 81 rows containing missing values (geom_text_repel).
plot_all_reps(value_col='vst_car_score') +
labs(title='VST-normalized CAR score replicate comparison')
## Warning in summary.lm(lm(x_scaled_sep ~ y_scaled_sep)): essentially perfect fit:
## summary may be unreliable
## Warning: Removed 1 rows containing missing values (geom_point).
## Warning: Removed 1 rows containing missing values (geom_text_repel).
## Warning: Removed 1 rows containing missing values (geom_text_repel).
plot_all_reps(value_col='rlog_car_score') +
labs(title='rlog-normalized CAR score replicate comparison')
## Warning in summary.lm(lm(x_scaled_sep ~ y_scaled_sep)): essentially perfect fit:
## summary may be unreliable
## Warning: Removed 1 rows containing missing values (geom_point).
## Warning: Removed 1 rows containing missing values (geom_text_repel).
## Warning: Removed 1 rows containing missing values (geom_text_repel).
ggplot(data=plot_all_reps(df=read.counts[(CAR.align != 'TNR8')],
value_col='car.abund.rel.baseline', df_only=T),
aes(x_scaled_sep, y_scaled_sep)) +
geom_point(shape = 21, colour = "grey30", aes(fill=rep_pair)) +
geom_text_repel(size=2.5, color='grey20', aes(label=label_outliers)) +
geom_text_repel(size=2.5, color='lightsalmon4', aes(label=label_arrayed)) +
theme_bw() +
facet_grid(k.type + t.type ~ assay)
## Warning: Removed 1 rows containing missing values (geom_point).
## Warning: Removed 1 rows containing missing values (geom_text_repel).
## Warning: Removed 1 rows containing missing values (geom_text_repel).
ggplot(data=plot_all_reps(df=read.counts[(CAR.align != 'TNR8')],
value_col='car.abund.rel.prev', df_only=T),
aes(x_scaled_sep, y_scaled_sep)) +
geom_point(shape = 21, colour = "grey30", aes(fill=rep_pair)) +
geom_text_repel(size=2.5, color='grey20', aes(label=label_outliers)) +
geom_text_repel(size=2.5, color='lightsalmon4', aes(label=label_arrayed)) +
theme_bw() +
facet_grid(k.type + t.type ~ assay)
## Warning in min(x): no non-missing arguments to min; returning Inf
## Warning in max(x): no non-missing arguments to max; returning -Inf
## Warning in min(x): no non-missing arguments to min; returning Inf
## Warning in max(x): no non-missing arguments to max; returning -Inf
## Warning: Removed 79 rows containing missing values (geom_point).
## Warning: Removed 79 rows containing missing values (geom_text_repel).
## Warning: Removed 79 rows containing missing values (geom_text_repel).
ggplot(data=plot_all_reps(df=read.counts,
value_col='rlog_car_score', df_only=T),
aes(x_scaled_sep, y_scaled_sep)) +
geom_point(shape = 21, colour = "grey30", aes(fill=rep_pair)) +
geom_text_repel(size=2.5, color='grey20', aes(label=label_outliers)) +
geom_text_repel(size=2.5, color='lightsalmon4', aes(label=label_arrayed)) +
theme_bw() +
facet_grid(k.type + t.type ~ assay)
## Warning: Removed 1 rows containing missing values (geom_point).
## Warning: Removed 1 rows containing missing values (geom_text_repel).
## Warning: Removed 1 rows containing missing values (geom_text_repel).
For figure 2B, we will combine rlog_car_score for CTV1 & 2 with baseline abundance for CTV3.
comp.df <- copy(read.counts[assay != 'baseline' & batch != 'post-cytof'])
comp.df[, car.abund.rel.prev.scaled := scales::rescale(car.abund.rel.prev),
by=sort.group]
## Warning in min(x): no non-missing arguments to min; returning Inf
## Warning in max(x): no non-missing arguments to max; returning -Inf
comp.df[, car.abund.rel.baseline.scaled := scales::rescale(car.abund.rel.baseline),
by=sort.group]
comp.df[, CAR.score.scaled := scales::rescale(CAR.score),
by=sort.group]
comp.df[, vst_car_score.scaled := scales::rescale(vst_car_score),
by=sort.group]
comp.df[, rlog_car_score.scaled := scales::rescale(rlog_car_score),
by=sort.group]
baseline.abund <- read.counts[
assay=='baseline', list(mean.baseline= mean(car.abund.baseline, na.rm=T)),
by=c('t.type','CAR.align')][,
list(CAR.align,
rel.baseline.log= log10(mean.baseline/mean(mean.baseline))),
by=c('t.type')]
comp.df <-comp.df[baseline.abund, on=c('t.type','CAR.align')]
plot_measure_pair <- function(df=comp.df, measure_i, measure_j) {
# fix overplotting:
df <- unique(df[!is.na(get(measure_i)) & !is.na(get(measure_j)), list(
get(measure_i), get(measure_j),
rel.baseline.log, CAR.align,
batch, donor, k.type, t.type, assay)])
names(df)[c(1,2)] <- c(measure_i, measure_j)
# use malanhanobis distance to collapse replicates,
# then use median absolute deviation to identify outliers
df[, outlier := {
#print(.BY)
#print(tibble(get(measure_i), get(measure_j)))
nonsingular <- apply(tibble(get(measure_i), get(measure_j)), 2,
function (x) var(x) > 0 & !is.na(var(x)))
non_singular_sd <- tibble(get(measure_i), get(measure_j))[,
(nonsingular)]
# if both measures are singular
if (!any(nonsingular)) (FALSE)
# for specific base of prev vs baseline and CTV1, will be singular
else if ((ncol(non_singular_sd) == 2 &&
all(non_singular_sd[,1] == non_singular_sd[,2])) |
ncol(non_singular_sd) != 2) {
!top_x_mad(pull(non_singular_sd, 1))
} else {
!isnt_out_maha(non_singular_sd, top_x_mad)
}
}, by=.(batch, donor, k.type, t.type, assay)]
df[, label_outliers := ''][outlier == T,
label_outliers := CAR.align]
df[, label_arrayed := ''][
CAR.align %in% array.list & !outlier,
label_arrayed := CAR.align]
r_squareds <- df[,
list(r=sqrt(summary(lm(data=.SD,
as.formula(paste(measure_i, "~", measure_j))))$r.squared)),
by=.(batch, donor, k.type, t.type, assay)]
r_squareds[, r_lbl := paste('r=',as.character(round(r, 2)))]
return(ggplot(df, aes_string(
x=measure_i, y=measure_j)) +
geom_point(data=df, shape = 21, colour = "grey30", aes(fill=rel.baseline.log)) +
geom_text_repel(data=df, size=2.5, color='grey20', aes(label=label_outliers)) +
geom_text_repel(data=df, size=2.5, color='lightsalmon4', aes(label=label_arrayed)) +
theme_bw() +
geom_label(size=2.5, aes(x=Inf, y=-Inf, label=r_lbl), data=r_squareds,
hjust=1, vjust=0) +
scale_fill_distiller(palette='BrBG',
limit=c(-1,1) * max(abs(df$rel.baseline.log))) +
facet_grid(batch+donor ~ k.type + t.type + assay) +
labs(title=paste(measure_i,'vs.',measure_j,'Individual replicates')))
}
deseq2_rc_measures <- comp.df[
dcast(all.deseq.results.dt[,
contrast := paste(ref_set, test_set, sep='_v_')][,
`:=`(log2FoldChange.scaled=scales::rescale(log2FoldChange)),
by=.(k.type, t.type, assay, contrast)],
k.type + t.type + assay + CAR.align ~ contrast,
value.var = c('log2FoldChange', 'padj')),
on=c('k.type','t.type','assay','CAR.align')]
plot_measure_pair(comp.df, 'vst_car_score.scaled', 'CAR.score.scaled')
## Warning in summary.lm(lm(data = .SD, as.formula(paste(measure_i, "~",
## measure_j)))): essentially perfect fit: summary may be unreliable
plot_measure_pair(comp.df, 'rlog_car_score.scaled', 'CAR.score.scaled')
## Warning in summary.lm(lm(data = .SD, as.formula(paste(measure_i, "~",
## measure_j)))): essentially perfect fit: summary may be unreliable
plot_measure_pair(comp.df, 'vst_car_score.scaled', 'rlog_car_score.scaled')
## Warning in summary.lm(lm(data = .SD, as.formula(paste(measure_i, "~",
## measure_j)))): essentially perfect fit: summary may be unreliable
plot_measure_pair(comp.df, 'CAR.score.scaled', 'car.abund.rel.baseline.scaled')
## Warning in summary.lm(lm(data = .SD, as.formula(paste(measure_i, "~",
## measure_j)))): essentially perfect fit: summary may be unreliable
plot_measure_pair(comp.df, 'vst_car_score.scaled', 'car.abund.rel.baseline.scaled')
## Warning in summary.lm(lm(data = .SD, as.formula(paste(measure_i, "~",
## measure_j)))): essentially perfect fit: summary may be unreliable
plot_measure_pair(comp.df, 'rlog_car_score.scaled', 'car.abund.rel.baseline.scaled')
## Warning in summary.lm(lm(data = .SD, as.formula(paste(measure_i, "~",
## measure_j)))): essentially perfect fit: summary may be unreliable
plot_measure_pair(comp.df, 'CAR.score.scaled', 'car.abund.rel.prev.scaled')
## Warning in summary.lm(lm(data = .SD, as.formula(paste(measure_i, "~",
## measure_j)))): essentially perfect fit: summary may be unreliable
plot_measure_pair(comp.df, 'vst_car_score.scaled', 'car.abund.rel.prev.scaled')
## Warning in summary.lm(lm(data = .SD, as.formula(paste(measure_i, "~",
## measure_j)))): essentially perfect fit: summary may be unreliable
plot_measure_pair(comp.df, 'rlog_car_score.scaled', 'car.abund.rel.prev.scaled')
## Warning in summary.lm(lm(data = .SD, as.formula(paste(measure_i, "~",
## measure_j)))): essentially perfect fit: summary may be unreliable
plot_measure_pair(comp.df, 'car.abund.rel.baseline.scaled', 'car.abund.rel.prev.scaled')
## Warning in summary.lm(lm(data = .SD, as.formula(paste(measure_i, "~",
## measure_j)))): essentially perfect fit: summary may be unreliable
## Warning in summary.lm(lm(data = .SD, as.formula(paste(measure_i, "~",
## measure_j)))): essentially perfect fit: summary may be unreliable
## Warning in summary.lm(lm(data = .SD, as.formula(paste(measure_i, "~",
## measure_j)))): essentially perfect fit: summary may be unreliable
## Warning in summary.lm(lm(data = .SD, as.formula(paste(measure_i, "~",
## measure_j)))): essentially perfect fit: summary may be unreliable
## Warning in summary.lm(lm(data = .SD, as.formula(paste(measure_i, "~",
## measure_j)))): essentially perfect fit: summary may be unreliable
## Warning in summary.lm(lm(data = .SD, as.formula(paste(measure_i, "~",
## measure_j)))): essentially perfect fit: summary may be unreliable
## Warning in summary.lm(lm(data = .SD, as.formula(paste(measure_i, "~",
## measure_j)))): essentially perfect fit: summary may be unreliable
## Warning in summary.lm(lm(data = .SD, as.formula(paste(measure_i, "~",
## measure_j)))): essentially perfect fit: summary may be unreliable
## Warning in summary.lm(lm(data = .SD, as.formula(paste(measure_i, "~",
## measure_j)))): essentially perfect fit: summary may be unreliable
## Warning in summary.lm(lm(data = .SD, as.formula(paste(measure_i, "~",
## measure_j)))): essentially perfect fit: summary may be unreliable
## Warning in summary.lm(lm(data = .SD, as.formula(paste(measure_i, "~",
## measure_j)))): essentially perfect fit: summary may be unreliable
plot_measure_pair(deseq2_rc_measures,
'log2FoldChange_D_v_A',
'car.abund.rel.prev.scaled') +
labs(title='DeSeq2 D:A vs Abundance-Prev')
plot_measure_pair(deseq2_rc_measures,
'log2FoldChange_D_v_A',
'car.abund.rel.prev.scaled') +
labs(title='DeSeq2 D:A vs Abundance-Prev')
plot_measure_pair(deseq2_rc_measures,
'log2FoldChange_D_v_A',
'car.abund.rel.baseline.scaled') +
labs(title='DeSeq2 D:A vs Abundance-Baseline')
plot_measure_pair(deseq2_rc_measures,
'log2FoldChange_CD_v_AB',
'car.abund.rel.prev.scaled') +
labs(title='DeSeq2 CD:AB vs Abundance-Prev')
plot_measure_pair(deseq2_rc_measures,
'log2FoldChange_CD_v_AB',
'car.abund.rel.baseline.scaled') +
labs(title='DeSeq2 CD:AB vs Abundance-Baseline')
plot_measure_pair(deseq2_rc_measures[, log2FoldChange_CD_v_A.scaled := scales::rescale(log2FoldChange_CD_v_A), by=.(k.type, t.type, assay)],
'log2FoldChange_CD_v_A.scaled',
'rlog_car_score.scaled') +
labs(title='DeSeq2 CD:A vs R-log-scaled CAR score')
## Warning in min(x): no non-missing arguments to min; returning Inf
## Warning in max(x): no non-missing arguments to max; returning -Inf
## Warning in min(x): no non-missing arguments to min; returning Inf
## Warning in max(x): no non-missing arguments to max; returning -Inf
The final plot - r-log-scaled car score to CD:A deseq ratio, seems to be the best correspondence I have seen.
Not using this right now.
# bin_pcts <- dcast(
# comp.df[,
# list(bin, cell.count, car.bin.pct, sort.group,
# assay_group= paste(.BY[c(2,3,4)], collapse='_')),
# by=c('CAR.align','assay','t.type','k.type')],
# CAR.align + assay + t.type + k.type + sort.group + assay_group ~ cell.count,
# value.var='car.bin.pct')
#
# cell_counts <- dcast(comp.df[,
# list(bin, cell.count, car.bin.pct, sort.group, car.bin.read.pct,
# assay_group= paste(.BY[c(2,3,4)], collapse='_')),
# by=c('CAR.align','assay','t.type','k.type')][,
# cell.bin.count := round(car.bin.read.pct * cell.count)],
# CAR.align + assay + t.type + k.type + sort.group ~ bin,
# value.var='cell.bin.count')
#
# cell_totals <- dcast(
# unique(comp.df[, list(bin, cell.count, sort.group)][,
# bin := paste(bin,'total',sep='.')])[,
# cell.pct := cell.count/sum(cell.count, na.rm=T), by=c('sort.group')],
# sort.group ~ bin,
# value.var='cell.pct')
#
# cell_pcts <- dcast(
# cell_counts[, data.table(bins=c('A','B','C','D'),
# c(A,B,C,D)/sum(c(A,B,C,D), na.rm=T)), by=.(CAR.align, sort.group)],
# CAR.align + sort.group ~ bins,
# value.var='V2')
#
# cell_counts[cell_pcts, on=c('CAR.align','sort.group')][cell_totals, on=c('sort.group')]
#
# # cell_counts[cell_pcts, on=c('CAR.align','sort.group')][
# # cell_totals, on=c('sort.group')][
# # sort.group == 'prolif2_d2_4d_CTV1_CD8_pos' & CAR.align == 'TNR8', list(
# # theta_hat= ddirichlet(c(i.A, i.B, i.C, i.D), c(A, B, C, D)),
# # theta=ddirichlet(c(A.total, B.total, C.total, D.total), c(A, B, C, D))),
# # by=c('CAR.align','sort.group')]
all_rep_comparisons <- read.counts[
grepl('CTV', assay),
data.table(matrix(combn(unique(sort.group), 2), ncol=2, byrow=T)),
by=c('assay','t.type')]
names(all_rep_comparisons)[3:4] <- c('x.group','y.group')
all_rep_comparisons <- all_rep_comparisons[,
cast_comparison(read.counts, x.group, y.group)[,
c('x.group','y.group') := NULL],
by=c('assay','t.type','x.group','y.group')]
ggplot(all_rep_comparisons[assay == 'CTV3']) +
geom_point(aes(x_scaled_sep, y_scaled_sep)) +
facet_wrap(assay + t.type ~ x.group + y.group)
## Warning: Removed 3 rows containing missing values (geom_point).
all_rep_comparisons <- read.counts[
grepl('CTV', assay),
data.table(matrix(combn(unique(sort.group), 2), ncol=2, byrow=T)),
by=c('assay','k.type')]
names(all_rep_comparisons)[3:4] <- c('x.group','y.group')
all_rep_comparisons <- all_rep_comparisons[,
cast_comparison(read.counts, x.group, y.group)[,
c('x.group','y.group') := NULL],
by=c('assay','x.group','y.group')]
ggplot(all_rep_comparisons[assay == 'CTV3']) +
geom_point(aes(x_scaled_sep, y_scaled_sep)) +
facet_wrap(assay ~ x.group + y.group)
## Warning: Removed 4 rows containing missing values (geom_point).
# remove prolif1.d2.ctv3 for now - CD4 has only 1 bin, D,
# and KLRG1 is on top for CD19+ ...need to check this more
pos_neg_comparisons <- read.counts[grepl('CTV', assay), {
if (length(unique(sort.group)) == 2)
cast_comparison(
.SD,
unique(sort.group[k.type == 'neg']),
unique(sort.group[k.type == 'pos']))
},
by=c('donor','assay','batch','t.type')][
!(donor == 'd2' & batch == 'prolif1' & assay == 'CTV3')]
ggplot(pos_neg_comparisons[, prolif_donor := interaction(batch, donor)],
aes(x=x_scaled_comb, y=y_scaled_comb, label=CAR.align)) +
geom_point() +
facet_grid(prolif_donor+t.type~assay) +
geom_text_repel(data=pos_neg_comparisons[,
prolif_donor := interaction(batch, donor)][
CAR.align %in% c('BAFF-R','TNR8','4-1BB','TACI','CD28','KLRG1','CD40')]) +
labs(x='CD19- Proliferation (+/- scaled together, per replicate)',
y='CD19+ Proliferation (+/- scaled together, per replicate)')
ggplot(pos_neg_comparisons[, prolif_donor := interaction(batch, donor)],
aes(x=x_scaled_comb/y_scaled_comb, y=y_scaled_sep, label=CAR.align)) +
geom_point() +
facet_grid(prolif_donor+t.type~assay) +
geom_text_repel(data=pos_neg_comparisons[,
prolif_donor := interaction(batch, donor)][
CAR.align %in% c('BAFF-R','TNR8','4-1BB','TACI','CD28','KLRG1','CD40')]) +
labs(x='CD19+ Proliferation (+ scaled alone, per replicate)',
y='CD19- : CD19+ Proliferation Ratio')
pos_neg_comparisons_melt <- melt(
pos_neg_comparisons[,
xy_ratio := x_scaled_comb/y_scaled_comb], measure.vars= grep(
'^[xy][^.]', names(pos_neg_comparisons), perl=T, value=T)
)[,
`:=`(var_mean= mean(value, na.rm=T), var_sd= sd(value, na.rm=T)),
by=c('t.type','assay','CAR.align','variable')]
cast_left <- paste(names(pos_neg_comparisons_melt)[1:6], collapse = ' + ')
cast_right <- 'variable'
cast_formula <- paste(cast_left, cast_right, sep= ' ~ ')
pos_neg_assay_avg <- dcast(
pos_neg_comparisons_melt, cast_formula, value.var='var_mean')
pos_neg_assay_plot <- unique(
pos_neg_assay_avg[, list(t.type, assay, xy_ratio, y_scaled_sep, CAR.align)])
pos_neg_assay_plot[, label.few := '']
pos_neg_assay_plot[
CAR.align %in% c('BAFF-R','TNR8','4-1BB','TACI','CD28','KLRG1','CD40'),
label.few := CAR.align]
ggplot(pos_neg_assay_plot,
aes(x=xy_ratio, y=y_scaled_sep, label=label.few)) +
geom_point() +
facet_wrap(t.type ~ assay) +
geom_text_repel() +
labs(
title='Average Prolifertation and -/+ ratio across replicates',
y='CD19+ Proliferation (scaled for replicate)',
x='CD19- : CD19+ Proliferation Ratio')
ggplotly(ggplot(pos_neg_assay_plot,
aes(x=xy_ratio, y=y_scaled_sep, label=CAR.align)) +
geom_point() +
facet_wrap(t.type ~ assay) +
labs(
title='Average Prolifertation and -/+ ratio across replicates (interactive)',
y='CD19+ Proliferation (scaled for replicate)',
x='CD19- : CD19+ Proliferation Ratio'))
ggplot_pos_neg_assay <- ggplot(pos_neg_assay_plot,
aes(x=xy_ratio, y=y_scaled_sep, label=label.few)) +
geom_point() +
facet_grid(t.type ~ assay) +
scale_x_continuous(labels=percent) +
geom_text_repel(size=2.5) +
geom_vline(xintercept = -Inf) + geom_hline(yintercept = -Inf) +
theme_minimal() +
labs(
title='Average Prolifertation and -/+ ratio across replicates',
y='CD19+ Proliferation (Scaled)',
x='Relative Nonspecific Proliferation')
ggplot_pos_neg_assay
ggsave(here::here('..','figs','pooled','relative_prolif.pdf'),
ggplot_pos_neg_assay, height=4, width=7, useDingbats=FALSE)
pos_neg_comparisons_melt <- melt(
pos_neg_comparisons[,
xy_ratio := x_scaled_comb/y_scaled_comb], measure.vars= grep(
'^[xy][^.]', names(pos_neg_comparisons), perl=T, value=T)
)[,
`:=`(var_mean= mean(value, na.rm=T), var_sd= sd(value, na.rm=T)),
by=c('t.type','CAR.align','variable')]
cast_left <- paste(names(pos_neg_comparisons_melt)[1:6], collapse = ' + ')
cast_right <- 'variable'
cast_formula <- paste(cast_left, cast_right, sep= ' ~ ')
pos_neg_assay_avg <- dcast(
pos_neg_comparisons_melt, cast_formula, value.var='var_mean')
pos_neg_assay_plot <- unique(
pos_neg_assay_avg[, list(t.type, xy_ratio, y_scaled_sep, CAR.align)])
pos_neg_assay_plot[, label.few := '']
pos_neg_assay_plot[
CAR.align %in% c('BAFF-R','TNR8','4-1BB','TACI','CD28','KLRG1','CD40'),
label.few := CAR.align]
ggplot_pos_neg_assay <- ggplot(pos_neg_assay_plot,
aes(x=xy_ratio, y=y_scaled_sep, label=label.few)) +
geom_point() +
facet_grid(t.type ~ .) +
scale_x_continuous(labels=percent) +
geom_text_repel(size=2.5) +
geom_vline(xintercept = -Inf) + geom_hline(yintercept = -Inf) +
theme_minimal() +
labs(
title='Average Prolifertation and -/+ ratio across replicates',
y='CD19+ Proliferation (Scaled)',
x='Relative Nonspecific Proliferation')
##TODO Change labelled CARs to ones significant in the CD19+ panel
ggsave(here::here('..','figs','pooled','relative_prolif_t_mean.pdf'),
ggplot_pos_neg_assay, height=4, width=7, useDingbats=FALSE)
pos_neg_comparisons_melt <- melt(
pos_neg_comparisons[,
xy_ratio := x_scaled_comb/y_scaled_comb], measure.vars= grep(
'^[xy][^.]', names(pos_neg_comparisons), perl=T, value=T)
)[,
`:=`(var_mean= mean(value, na.rm=T), var_sd= sd(value, na.rm=T)),
by=c('t.type','CAR.align','variable')]
cast_left <- paste(names(pos_neg_comparisons_melt)[1:6], collapse = ' + ')
cast_right <- 'variable'
cast_formula <- paste(cast_left, cast_right, sep= ' ~ ')
pos_neg_assay_avg <- dcast(
pos_neg_comparisons_melt, cast_formula, value.var='var_mean')
pos_neg_assay_plot <- unique(
pos_neg_assay_avg[, list(t.type, x_scaled_comb, y_scaled_comb, CAR.align)])
pos_neg_assay_plot[, label.few := '']
pos_neg_assay_plot[
CAR.align %in% c('BAFF-R','TNR8','4-1BB','TACI','CD28','KLRG1','CD40'),
label.few := CAR.align]
# simpler plot
ggplot_pos_neg_assay <- ggplot(pos_neg_assay_plot,
aes(x=x_scaled_comb, y=y_scaled_comb, label=label.few)) +
geom_point() +
facet_grid(t.type ~ .) +
scale_x_continuous(expand=expansion(mult=1, add=0), limits=c(0, NA)) +
expand_limits(x=0) +
geom_text_repel(size=2.5) +
geom_vline(xintercept = -Inf) + geom_hline(yintercept = -Inf) +
theme_minimal() +
labs(
title='Proliferation, +/- antigen',
y='CD19+ Proliferation (scaled per replicate)',
x='CD19- Proliferation (scaled per replicate)')
# with axis cuts
ggplot_pos_neg_assay <- ggplot(pos_neg_assay_plot,
aes(x=x_scaled_comb, y=y_scaled_comb, label=label.few)) +
geom_point() +
facet_grid(t.type ~ .) +
scale_x_continuous(expand=expansion(mult=c(0,.1), add=0), limits=c(0,NA)) +
scale_y_continuous(limits=c(0.48, 0.95), expand=expansion(mult=c(0,0), add=0),
breaks=(5:10)/10, labels=c('', (6:10)/10)) +
geom_text_repel(size=2.5) +
geom_vline(xintercept = -Inf) + geom_hline(yintercept = -Inf) +
labs(
title='Proliferation, +/- antigen',
y='CD19+ Proliferation (scaled per replicate)',
x='CD19- Proliferation\n(scaled per replicate)') +
theme_minimal() +
theme(panel.spacing.y=unit(1.5, "lines"))
# fig2 style
# with axis cuts
ggplot_pos_neg_assay <- ggplot(pos_neg_assay_plot,
aes(x=x_scaled_comb, y=y_scaled_comb, label=label.few)) +
geom_point() +
facet_grid(t.type ~ .) +
scale_x_continuous(expand=expansion(mult=c(0,.1), add=0), limits=c(0,NA)) +
scale_y_continuous(limits=c(0.48, 0.95), expand=expansion(mult=c(0,0), add=0),
breaks=(5:10)/10, labels=c('', (6:10)/10)) +
geom_text_repel(size=2.5) +
geom_vline(xintercept = -Inf) + geom_hline(yintercept = -Inf) +
labs(
title='Proliferation, +/- antigen',
y='CD19+ Proliferation (scaled per replicate)',
x='CD19- Proliferation\n(scaled per replicate)') +
theme_minimal() +
theme(panel.spacing.y=unit(1.5, "lines"))
ggsave(here::here('..','figs','pooled','scaled_prolif_t_mean.pdf'),
ggplot_pos_neg_assay, height=6, width=3.6, useDingbats=FALSE)
pos_neg_comparisons_melt <- melt(
pos_neg_comparisons[,
xy_ratio := x_scaled_comb/y_scaled_comb], measure.vars= grep(
'^[xy][^.]', names(pos_neg_comparisons), perl=T, value=T)
)[,
`:=`(var_mean= mean(value, na.rm=T), var_sd= sd(value, na.rm=T)),
by=c('t.type','CAR.align','variable', 'assay')]
cast_left <- paste(names(pos_neg_comparisons_melt)[1:6], collapse = ' + ')
cast_right <- 'variable'
cast_formula <- paste(cast_left, cast_right, sep= ' ~ ')
pos_neg_assay_avg <- dcast(
pos_neg_comparisons_melt, cast_formula, value.var='var_mean')
pos_neg_assay_plot <- unique(
pos_neg_assay_avg[, list(t.type, x_scaled_comb, y_scaled_comb, CAR.align, assay)])
pos_neg_assay_plot[, label.few := '']
pos_neg_assay_plot[
CAR.align %in% c('BAFF-R','TNR8','4-1BB','TACI','CD28','KLRG1','CD40'),
label.few := CAR.align]
ggplot_pos_neg_assay <- ggplot(pos_neg_assay_plot[assay == 'CTV1'],
aes(x=x_scaled_comb, y=y_scaled_comb, label=label.few)) +
geom_point() +
facet_grid(t.type ~ .) +
scale_x_continuous() +
geom_text_repel(size=2.5) +
geom_vline(xintercept = -Inf) + geom_hline(yintercept = -Inf) +
theme_minimal() +
labs(
title='Average Prolifertation and -/+ ratio across replicates',
y='CTV1 CD19+ Proliferation (Scaled)',
x='CTV1 CD19- Proliferation (Scaled)')
ggsave(here::here('..','figs','pooled','scaled_prolif_t_mean_ctv1.pdf'),
ggplot_pos_neg_assay, height=4, width=7, useDingbats=FALSE)
ggplotly(make_volcanoes(all.deseq.results.dt[
k.type == 'pos' & ref_set == 'baseline']),
tooltip = "label", session='knitr')
ggplotly(make_cd4_cd8(all.deseq.results.dt[
k.type == 'pos' & ref_set == 'baseline']),
tooltip = "label", session='knitr')
ggplotly(make_timeseries(all.deseq.results.dt[
k.type == 'pos' & ref_set == 'baseline']),
tooltip = "label", session='knitr')
ggplotly(make_pos_neg(all.deseq.results.dt[
ref_set == 'baseline']),
tooltip = "label", session='knitr')
ggplotly(make_volcanoes(all.deseq.results.dt[
k.type == 'pos' & ref_set != 'baseline']),
tooltip = "label", session='knitr')
ggplotly(make_cd4_cd8(all.deseq.results.dt[
k.type == 'pos' & ref_set != 'baseline']),
tooltip = "label", session='knitr')
ggplotly(make_timeseries(all.deseq.results.dt[
k.type == 'pos' & ref_set != 'baseline']),
tooltip = "label", session='knitr')
ggplotly(make_pos_neg(all.deseq.results.dt[
ref_set == 'baseline']),
tooltip = "label", session='knitr')
ggplotly(make_pos_neg(all.deseq.results.dt[
ref_set != 'baseline']),
tooltip = "label", session='knitr')
data.output.dir <- file.path(here::here(
'..','..',
's3-roybal-tcsl',
'lenti_screen_compiled_data','data'))
load(
file=file.path(data.output.dir, 'pooled_deseq2_data.Rdata'))
all.deseq.results.dt[, padj.disp := -log10(padj)]
all.deseq.results.dt[, lfc.disp := log2FoldChange]
all.deseq.results.dt[padj.disp > 10, padj.disp := Inf]
all.deseq.results.dt[abs(lfc.disp) > 5, lfc.disp := sign(lfc.disp) * Inf]
data.dt <- all.deseq.results.dt
data.dt[, CAR.type := 'other']
data.dt[CAR.align == '4-1BB', CAR.type := '4-1BB']
data.dt[CAR.align == 'CD28', CAR.type := 'CD28']
data.dt[, CAR.type := factor(
CAR.type,levels=c('other','4-1BB','CD28'))]
# combined rlog car score for CTV1 & CTV2, baseline abundance for CTV3
fig2_rep_comp <- rbind(
plot_all_reps(
df=read.counts[batch != 'post-cytof'],
value_col='rlog_car_score', df_only=T)[assay == 'CTV1'],
plot_all_reps(
df=read.counts[batch != 'post-cytof' & CAR.align != 'TNR8'],
value_col='car.abund.rel.baseline', df_only=T)[assay != 'CTV1'])
fig2_rep_comp[, assay := factor(assay,
labels=c('Initial Proliferation\n(d0-d3/d4)',
'Cumulative Proliferation\n(d0-d16)',
'Cumulative Proliferation,\n(d0-d24)'))]
replicate_comp_plot <- ggplot(data=fig2_rep_comp,
aes(x_scaled_sep, y_scaled_sep)) +
geom_point(aes(color=interaction(rep_pair, k.type)), size=0.8) +
theme_minimal() +
theme(panel.border=element_rect(fill=NA)) +
facet_grid(t.type ~ assay) +
scale_color_manual('',
values=brewer.pal(11,'PRGn')[c(2,3,4,10,9,8)],
labels=c(
'CD19-, A v B', 'CD19-, A v C', 'CD19-, B v C',
'CD19+, A v B', 'CD19+, A v C', 'CD19+, B v C')) +
labs(x='Scaled replicate 1', y='Scaled replicate 2')
replicate_comp_plot
## Warning: Removed 1 rows containing missing values (geom_point).
ggsave(here::here('..','figs','pooled','pooled_replicate_comparison.pdf'),
replicate_comp_plot, height=4, width=7, useDingbats=FALSE)
## Warning: Removed 1 rows containing missing values (geom_point).
max_pval_y <- 6.5
#CD28 and 4-1BB Colors
receptor_cols <- RColorBrewer::brewer.pal(9, 'YlGnBu')[c(6,8)]
# label significant hits
data.dt[, CAR.type.size := CAR.type]
data.dt[CAR.type == 'other' &
((padj < 0.05 & log2FoldChange > 0) |
(padj < 0.05 & log2FoldChange > 0)),
CAR.type.size := 'other_sig']
data.dt[, CAR.label.sig := '']
data.dt[CAR.type.size != 'other',
CAR.label.sig := CAR.align]
# plot subset - CTV1, CTV2, and cumulative of all 3
data.subset.dt <- data.dt[
k.type == 'pos' &
((ref_set == 'baseline' & test_set == 'A' & assay != 'CTV1') |
(ref_set == 'CD' & test_set == 'A' & assay == 'CTV1'))]
# rename facets
data.subset.dt[, assay := factor(assay,
labels=c('Initial Proliferation\n(d0-d3/d4)',
'Cumulative Proliferation\n(d0-d16)',
'Cumulative Proliferation\n(d0-d24)'))]
# max out at log10-15
data.subset.dt[, padj.disp := -log10(padj)]
data.subset.dt[, CAR.pvalmax := padj.disp > max_pval_y]
data.subset.dt[padj.disp > max_pval_y, padj.disp := max_pval_y-0.18]
data.subset.dt[, order := ifelse(CAR.type=="other", 2, 1)]
interbin_volcano <- ggplot(data.subset.dt[order(CAR.type)],
aes(
x=log2FoldChange, y=padj.disp,
color=CAR.type.size,
fill=CAR.type.size,
label=CAR.label.sig,
size=CAR.type.size,
shape=CAR.pvalmax)) +
geom_point() +
facet_grid(t.type ~ assay) +
scale_color_manual('',
labels=c('New Receptors', '4-1BB', 'CD28', 'New Receptors'),
values=c('grey50', receptor_cols, outlier_cols[2]),
guide=F) +
scale_fill_manual('',
labels=c('New Receptors', '4-1BB', 'CD28', 'New Receptors'),
values=c('grey50', receptor_cols, outlier_cols[2]),
guide=F) +
scale_shape_manual(values=c(21,24), guide=F) +
scale_size_manual('',
labels=c('New Receptors', '4-1BB', 'CD28', 'New Receptors'),
values=c(1.5,3,3,2.5), guide=F) +
geom_hline(yintercept=-log10(0.05), linetype = 'dashed', alpha=.3) +
labs(x='Relative Proliferation,\nlog2 FC',
y='-log10(p)') +
scale_y_continuous(limits=c(0, max_pval_y), expand=c(.05,.05,0,0)) +
scale_x_continuous(limits=c(-3.25,3.25), breaks=c(-3:3)) +
geom_text_repel(size=3, point.padding=0.25, text.padding=0.25, segment.color='grey') +
theme_minimal() +
theme(panel.border=element_rect(fill=NA))
## Warning: Ignoring unknown parameters: text.padding
ggsave(here::here('..','figs','pooled','interbin_volcano.pdf'),
interbin_volcano, height=4, width=7, useDingbats=FALSE)
interbin_volcano
cast_4v8 <- dcast(data.dt[
k.type == 'pos' &
((ref_set == 'baseline' & test_set == 'A' & assay != 'CTV1') |
(ref_set == 'CD' & test_set == 'A' & assay == 'CTV1'))],
CAR.align + assay + CAR.type ~ t.type,
value.var = c("log2FoldChange", "padj.disp"))
# rename facets
cast_4v8[, assay := factor(assay,
labels=c('Initial Proliferation\n(d0-d3/d4)',
'Cumulative Proliferation\n(d0-d16)',
'Cumulative Proliferation,\n(d0-d24)'))]
# label significant hits
cast_4v8[, CAR.type.size := CAR.type]
cast_4v8[CAR.type == 'other' &
((padj.disp_CD4 > -log10(0.05) & log2FoldChange_CD4 > 0) |
(padj.disp_CD8 > -log10(0.05) & log2FoldChange_CD8 > 0)),
CAR.type.size := 'other_sig']
cast_4v8[, CAR.label.sig := '']
cast_4v8[CAR.type.size != 'other',
CAR.label.sig := CAR.align]
prolif_4v8 <- ggplot(cast_4v8[order(CAR.type)],
aes(y=log2FoldChange_CD8, x=log2FoldChange_CD4,
color=CAR.type,
label=CAR.label.sig,
size=CAR.type.size)) +
geom_point() +
facet_grid(. ~ assay) +
scale_color_manual('',
labels=c('New Receptors', '4-1BB', 'CD28'),
values=c('grey50', receptor_cols), guide=F) +
scale_size_manual('',
labels=c('New Receptors', '4-1BB', 'CD28', 'New Receptors'),
values=c(0.8,3,3,2.5), guide=F) +
geom_abline(linetype='dashed') +
geom_text_repel() +
theme_minimal() +
labs(x='CD4, relative log2 fold change',
y='CD8, relative log2 fold change') +
theme(panel.border=element_rect(fill=NA)) +
guides(size=F)
prolif_4v8
ggsave(here::here('..','figs','pooled','mixed_4v8.pdf'), prolif_4v8,
height=2.5, width=7)
cast_4v8[, mean_lfc := rowMeans(cbind(log2FoldChange_CD4, log2FoldChange_CD8))]
cast_4v8[, rel_8 := log2FoldChange_CD8 - log2FoldChange_CD4]
cast_4v8[CAR.type.size == 'other_sig' & xor(padj.disp_CD4 > -log10(0.05),
padj.disp_CD8 > -log10(0.05)), CAR.type.size := 'other_sig_diff']
prolif_4v8_rel <- ggplot(cast_4v8[order(CAR.type)],
aes(y=rel_8, x=mean_lfc,
color=CAR.type.size,
label=CAR.label.sig,
size=CAR.type.size)) +
annotate(geom='label', x=-Inf, y=Inf, label='CD8 Skewed',
label.r=unit(0,'pt'), size=3, vjust=.99, hjust=.01) +
annotate(geom='label', x=-Inf, y=-Inf, label='CD4 Skewed',
label.r=unit(0,'pt'), size=3, vjust=.01, hjust=.01) +
geom_point() +
facet_grid(. ~ assay, scales='free_x') +
scale_color_manual('',
labels=c('New Receptors', '4-1BB', 'CD28', 'New Receptors', 'New Receptors'),
values=c('grey50', receptor_cols, outlier_cols[2], outlier_cols[2]), guide=F) +
scale_size_manual('',
labels=c('New Receptors', '4-1BB', 'CD28', 'New Receptors', 'New Receptors'),
values=c(0.8,2.5,2.5,1,2.5), guide=F) +
scale_x_continuous(limits=c(-3.25,3.25), breaks=c(-3:3)) +
scale_y_continuous(limits=c(-2.25,1.9)) +
geom_hline(yintercept=0, linetype='dashed', alpha=.7) +
geom_text_repel(size=3, point.padding=0.25, segment.color='grey') +
theme_minimal() +
labs(y='log2 CD8:CD4 proliferation ratio',
x='Relative proliferation,\n log2 mean FC') +
theme(panel.border=element_rect(fill=NA)) +
guides(size=F)
ggsave(here::here('..','figs','pooled','mixed_4v8_rel.pdf'), prolif_4v8_rel,
height=2.5, width=7)
## Warning: Removed 1 rows containing missing values (geom_point).
## Warning: Removed 1 rows containing missing values (geom_text_repel).
prolif_4v8_rel
## Warning: Removed 1 rows containing missing values (geom_point).
## Warning: Removed 1 rows containing missing values (geom_text_repel).
cast_posneg <- dcast(data.dt[
((ref_set == 'baseline' & test_set == 'ABCD' & assay != 'CTV1') |
(ref_set == 'D' & test_set == 'A' & assay == 'CTV1'))],
CAR.align + assay + CAR.type + t.type ~ k.type,
value.var = c("log2FoldChange", "padj.disp"))
# rename facets
cast_posneg[, assay := factor(assay,
labels=c('Initial Proliferation\n(d0-d3/d4)',
'Cumulative Proliferation\n(d0-d16)',
'Cumulative Proliferation,\n(d0-d24)'))]
# label significant hits
cast_posneg[, CAR.type.size := CAR.type]
cast_posneg[CAR.type == 'other' & (
(2^(-padj.disp_pos) < 0.05 & log2FoldChange_pos > 0) |
(2^(-padj.disp_neg) < 0.05 & log2FoldChange_neg > 0)
),
CAR.type.size := 'other_sig']
prolif_posneg <- ggplot(cast_posneg,
aes(y=log2FoldChange_pos, x=log2FoldChange_neg,
color=CAR.type,
label=CAR.align,
size=CAR.type.size)) +
geom_point() +
facet_grid(t.type ~ assay) +
scale_color_manual('',
labels=c('New Receptors', '4-1BB', 'CD28'),
values=c('grey50', receptor_cols)) +
scale_size_manual('',
labels=c('New Receptors', '4-1BB', 'CD28', 'New Receptors'),
values=c(0.8,3,3,2)) +
geom_abline(linetype='dashed') +
theme_minimal() +
labs(y='Relative log2 fold change, with antigen',
x='Relative log2 fold change, no antigen') +
theme(panel.border=element_rect(fill=NA)) +
guides(size=F)
prolif_posneg
ggsave(here::here('..','figs','pooled','mixed_posneg.pdf'), prolif_posneg,
height=4.5, width=7)
##deseq2
all.deseq.results.dt[, comparison := paste(test_set,ref_set,sep= '.')]
deseq2_combined <- all.deseq.results.dt[,
comparison := paste(test_set, ref_set, sep='.')][,
combined_var := paste(comparison, assay)][, value := log2FoldChange]
deseq2_combined[, value.scale := scale(value), by = .(t.type, k.type, assay, comparison)]
deseq2_combined[,
car.axis := paste(CAR.align,t.type,k.type, sep='|'), by=.(t.type)]
## rlog car score
car_score_means <- read.counts[assay != 'baseline' & batch != 'post-cytof'][,
value.scale := scales::rescale(rlog_car_score), by=sort.group][,
value.mean := mean(value.scale, na.rm=T),
by=.(CAR.align, t.type, k.type, assay)][,
value.scale := scale(value.mean), by=.(t.type, k.type, assay)]
car_score_means[,
car.axis := paste(CAR.align,t.type,k.type, sep='|'), by=.(t.type)]
car_score_means[, comparison := 'rlog_car_score']
car_score_means[,
combined_var := paste(assay,comparison, sep='.'), by=.(t.type)]
# fc in proliferation
rel_abund_means <- read.counts[assay != 'baseline' & batch != 'post-cytof'][,
value.scale := scales::rescale(car.abund.rel.baseline), by=sort.group][,
value.mean := mean(value.scale, na.rm=T),
by=.(CAR.align, t.type, k.type, assay)][,
value.scale := scale(value.mean), by=.(t.type, k.type, assay)]
rel_abund_means[,
car.axis := paste(CAR.align,t.type,k.type, sep='|'), by=.(t.type)]
rel_abund_means[, comparison := 'abund_rel_baseline']
rel_abund_means[,
combined_var := paste(assay,comparison, sep='.'), by=.(t.type)]
### Cytokines
cytokine_ranks <- melt(
read.counts[batch == 'post-cytof' & assay != 'IL4'],
measure.vars = c('min.max.ratio.norm','all.max.ratio.norm','rlog_car_score'),
variable.name='comparison')[bin == 'A' & k.type == 'pos']
cytokine_ranks[, value.scale := scale(value), by=.(sort.group,comparison,t.type)]
cytokine_ranks[,
car.axis := paste(CAR.align,t.type,k.type, sep='|'), by=.(t.type)]
cytokine_ranks[,
combined_var := paste(assay,comparison, sep='.'), by=.(t.type)]
### Combined
shared_cols <- intersect(
intersect(names(deseq2_combined), names(cytokine_ranks)),
names(car_score_means))
total_combined <- unique(rbind(
deseq2_combined[, shared_cols, with=F],
cytokine_ranks[comparison == 'rlog_car_score', shared_cols, with=F],
car_score_means[comparison == 'rlog_car_score', shared_cols, with=F],
rel_abund_means[comparison == 'abund_rel_baseline', shared_cols, with=F],
use.names=F))
# relabel x axis assays
#total_combined[, assay := factor(assay,
# labels=c('CTV1','CTV2','CTV3','CD69','IFNy','IL2'))]
library(dendextend)
##
## ---------------------
## Welcome to dendextend version 1.13.4
## Type citation('dendextend') for how to cite the package.
##
## Type browseVignettes(package = 'dendextend') for the package vignette.
## The github page is: https://github.com/talgalili/dendextend/
##
## Suggestions and bug-reports can be submitted at: https://github.com/talgalili/dendextend/issues
## Or contact: <tal.galili@gmail.com>
##
## To suppress this message use: suppressPackageStartupMessages(library(dendextend))
## ---------------------
##
## Attaching package: 'dendextend'
## The following object is masked from 'package:data.table':
##
## set
## The following object is masked from 'package:stats':
##
## cutree
library(ggdendro)
##
## Attaching package: 'ggdendro'
## The following object is masked from 'package:dendextend':
##
## theme_dendro
library(cowplot)
##
## ********************************************************
## Note: As of version 1.0.0, cowplot does not change the
## default ggplot2 theme anymore. To recover the previous
## behavior, execute:
## theme_set(theme_cowplot())
## ********************************************************
library(gtable)
library(grid)
make_dendroheatmap <- function(
df, title, dendro_units=grid::unit(0.2, "null"),
legend_theme=theme(legend.position = "none"))
{
# Make dendrogram
car_mean_cast <- dcast(
df[k.type == 'pos'],
CAR.align ~ assay + t.type,
value.var='value.scale')
car_dendro_m <- as.matrix(car_mean_cast[, -c(1:2)])
rownames(car_dendro_m) <- unlist(car_mean_cast[,1])
car_dendro <- as.dendrogram(hclust(d = dist(x = car_dendro_m)))
car_dendro <- rotate(car_dendro, names(sort(rowMeans(car_dendro_m))))
# Create dendrogram plot
dendro_vars_plot <- ggdendrogram(data = car_dendro, rotate = TRUE) +
theme(axis.text.y = element_text(size = 6))
# row order
df$CAR.align <- factor(
df$CAR.align,
levels = labels(car_dendro))
#manual x labels
x_lab_man <- c('CD69 18h',
'Prolif d3-4','Prolif d14-16','Prolif d24',
'IFNy d3', 'IL2 d3', 'CD69 18h',
'Prolif d4','Prolif d14-16','Prolif d24')
# heatmap
var_heatmap <- ggplot(df) +
geom_tile(aes(x = interaction(assay,t.type), y = CAR.align,
fill = value.scale), colour = "black", size = .20) +
scale_fill_distiller('Z-score',
palette='PiYG', limits=c(-3,3), oob=scales::squish, direction=1) +
scale_x_discrete(expand=c(0,0),
labels=x_lab_man) +
scale_y_discrete(labels= (
function (breaks)
unlist(lapply(breaks, function(str)
strsplit(str, '|',fixed=T)[[1]][1])))) +
theme(axis.text.x = element_text(angle = 90, hjust = 1),
axis.title.x = element_blank(),
axis.title.y = element_blank()) +
labs(title=title)
var_heatmap <- var_heatmap + legend_theme
# col dendro
dendro_data_col <- dendro_data(car_dendro, type = "rectangle")
dendro_col <- axis_canvas(var_heatmap, axis = "y", coord_flip=T) +
geom_segment(data = segment(dendro_data_col),
aes(x = x, y = y, xend = xend, yend = yend)) +
coord_flip()
plot_dendroheat <- insert_yaxis_grob(var_heatmap,
dendro_col, dendro_units, position = "right")
return(ggdraw(plot_dendroheat))
}
# make_dendroheatmap(total_combined[xor(comparison == 'rlog_car_score', grepl('CTV',assay)) & k.type == 'pos'],
# title='Deseq2 for CTV, car score for markers')
# make_dendroheatmap(total_combined[comparison == 'rlog_car_score' & k.type == 'pos'],
# title='car score for all')
#
# make_dendroheatmap(total_combined[(assay == 'CTV1' & comparison == 'rlog_car_score') | (assay %in% c('CTV2','CTV3') & comparison == 'ABCD.baseline') | (!(assay %in% c('CTV1','CTV2','CTV3')) & comparison == 'rlog_car_score')],
# title='same_as_other_fig2_panels')
#
# make_dendroheatmap(total_combined[t.type == 'CD4' & ((assay == 'CTV1' & comparison == 'rlog_car_score') | (assay %in% c('CTV2','CTV3') & comparison == 'ABCD.baseline') | (!(assay %in% c('CTV1','CTV2','CTV3')) & comparison == 'rlog_car_score'))],
# title='same_as_other_fig2_panels - cd4 only')
#
# make_dendroheatmap(total_combined[t.type == 'CD8' & ((assay == 'CTV1' & comparison == 'rlog_car_score') | (assay %in% c('CTV2','CTV3') & comparison == 'ABCD.baseline') | (!(assay %in% c('CTV1','CTV2','CTV3')) & comparison == 'rlog_car_score'))],
# title='same_as_other_fig2_panels - cd8 only')
#
# make_dendroheatmap(total_combined[xor(comparison == 'rlog_car_score', grepl('CTV',assay)) & k.type == 'pos' & t.type == 'CD4'],
# title='CD4 only, Deseq2 for CTV, car score for markers')
# make_dendroheatmap(total_combined[comparison == 'rlog_car_score' & k.type == 'pos' & t.type == 'CD4'],
# title='CD4 only, car score for all')
#
# make_dendroheatmap(total_combined[xor(comparison == 'rlog_car_score', grepl('CTV',assay)) & k.type == 'pos' & t.type == 'CD8'],
# title='CD8 only, Deseq2 for CTV, car score for markers')
# make_dendroheatmap(total_combined[comparison == 'rlog_car_score' & k.type == 'pos' & t.type == 'CD8'],
# title='CD8 only, car score for all')
## combined 4 and 8 with separate dendrograms
#chosen metrics 1
# chosen_metrics <- total_combined[k.type == 'pos' &
# ((assay == 'CTV1' & comparison == 'A.CD') |
# (assay %in% c('CTV2','CTV3') & comparison == 'ABCD.baseline') |
# (!(assay %in% c('CTV1','CTV2','CTV3')) & comparison == 'rlog_car_score'))]
#chosen metrics 2
chosen_metrics <- total_combined[k.type == 'pos' &
((assay == 'CTV1' & comparison == 'A.baseline') |
(assay %in% c('CTV2','CTV3') & comparison == 'abund_rel_baseline') |
(!(assay %in% c('CTV1','CTV2','CTV3')) & comparison == 'rlog_car_score'))]
fig_2a_single_dendro <- make_dendroheatmap(
chosen_metrics,
title='',
legend_theme=theme(
legend.position = c(1, 0),
legend.justification = c(-1.7, 0),
plot.margin = unit(c(5.5,33,33,5.5), "pt")))
fig_2a_single_dendro
### proliferation plot
library(ggnewscale)
prolif_data <- read.counts[
(k.type =='pos' | is.na(k.type)) & batch != 'post-cytof'][,
list(batch, donor,
mean.car.abund.rel.baseline= mean(log2(car.abund.rel.baseline)),
sd.car.abund.rel.baseline= 2^sd(log2(car.abund.rel.baseline))),
by=c('CAR.align', 't.type','assay')]
#reorder CAR names to put CD28 and 41BB in front (at end of levels list)
new_car_levels <- prolif_data[, c(
levels(CAR.align)[!(levels(CAR.align) %in% c('4-1BB','CD28'))],
'4-1BB','CD28')]
prolif_data[, CAR.align := factor(CAR.align, new_car_levels)]
prolif_data[, CAR.type := 'other']
prolif_data[CAR.align == '4-1BB', CAR.type := '4-1BB']
prolif_data[CAR.align == 'CD28', CAR.type := 'CD28']
prolif_data[, CAR.type := factor(
CAR.type,levels=c('other','4-1BB','CD28'))]
top_num <- 6
label_data <- unique(prolif_data[assay=='CTV3',
list(t.type, CAR.align, mean.car.abund.rel.baseline, assay, CAR.type)])[,
car_order := rank(mean.car.abund.rel.baseline), by='t.type'][
car_order %in% c(1:(1+top_num),(40-top_num):40) |
CAR.align %in% c('CD28','41BB')]
#color labels pink vs green
label_data[, CAR.topbot := CAR.type]
label_data[car_order %in% 1:(1+top_num) & CAR.type == 'other',
CAR.topbot :='top']
label_data[car_order %in% (40-top_num):40 & CAR.type == 'other',
CAR.topbot :='bot']
label_data[, CAR.topbot := factor(CAR.topbot)]
fig2_abundance_over_time <- ggplot(prolif_data[order(CAR.type)], aes(
y=mean.car.abund.rel.baseline, x=assay,
color=CAR.type,
group=CAR.align,
label=CAR.align)) +
# line
geom_line(aes(size=CAR.type)) +
scale_size_manual('',
labels=c('New Receptors', '4-1BB', 'CD28'),
values=c(0.5,1.5,1.5), guide=F) +
scale_color_manual('',
labels=c('New Receptors', '4-1BB', 'CD28'),
values=c('grey30', receptor_cols),
guide=F) +
#points
new_scale('size') +
geom_point(aes(size=CAR.type, color=CAR.type, group=CAR.align)) +
scale_size_manual('',
labels=c('New Receptors', '4-1BB', 'CD28'),
values=c(1,2,2), guide=F) +
# text
new_scale('color') +
geom_text_repel(data=label_data,
aes(color=CAR.topbot),
nudge_x = 1.2,
direction = "y",
hjust = 1,
size = 3,
segment.size = 0.3,
segment.color='grey') +
scale_color_manual('',
labels=c('4-1BB', 'CD28','Top','Bottom'),
values=c(receptor_cols, outlier_cols),
guide=F) +
facet_grid(. ~ t.type) +
scale_x_discrete(
'Days of continuous CD19 stimulation',
expand = c(0, 0.4, 0, 1.4),
labels=c('Baseline', 'Day 3-4', 'Day 14-16', 'Day 24')) +
scale_y_continuous('Relative Proliferation,\n log2 FC') +
theme_minimal() +
theme(panel.border=element_rect(fill=NA))
ggsave(here::here('..','figs','pooled','abund_over_time.pdf'), fig2_abundance_over_time,
height=3, width=8)
fig2_abundance_over_time
# cd19+/cd19- cols
cytokine.pct.pos <- dcast(
read.counts[batch == "post-cytof" & t.type == 'CD4'][
bin %in% c('C','D'), list(pct_pos= sum(car.bin.pct)),
by=c('assay','CAR.align', 'k.type')],
assay + CAR.align ~ k.type, value.var='pct_pos')
cytokine.pct.pos[, CAR.align.assay := factor(paste(CAR.align,assay,sep='|'))]
cytokine.pct.pos[, CAR.align.assay := factor(
CAR.align.assay,
levels=levels(CAR.align.assay),
labels=cytokine.pct.pos[, gsub('(.*)\\..*', '\\1', levels(CAR.align.assay))])]
cytokine_panel <- ggplot(cytokine.pct.pos[assay %in% c("IL2","IFNy")]) +
geom_segment(aes(x = reorder(CAR.align.assay, neg-pos),
xend = reorder(CAR.align.assay, neg-pos),
y = neg,
yend = pos), color="grey") +
geom_point(
aes(x = reorder(CAR.align.assay, neg-pos), y = pos, color='CD19+'),
size=2.5) +
geom_point(
aes(x = reorder(CAR.align.assay, neg-pos), y = neg, color='CD19-'),
size=2.5) +
scale_color_manual(
values=c('CD19+'=cd19_val[2], 'CD19-'='grey30')) +
facet_wrap(~ assay, scales='free_x') +
scale_y_continuous(label=percent) +
scale_x_discrete(labels= (
function (breaks)
unlist(lapply(breaks, function(str)
strsplit(str, '|',fixed=T)[[1]][1])))) +
labs(y = '% Positive (ICS)') +
theme_minimal() +
theme(panel.border=element_rect(fill=NA),
axis.text.x = element_text(angle = 90, hjust = 1, vjust=0.5),
axis.title.x = element_blank(),
panel.grid.major.x = element_blank(),
strip.background = element_rect(colour="white", fill="white"),
legend.title = element_blank(),
legend.justification=c(1,1),
legend.position=c(0.99,1.01))
ggsave(here::here('..','figs','pooled','cytokine_plot.pdf'), cytokine_panel,
height=3, width=8)
cytokine_panel
### Combined panels for figure 2
#old version with replicate comparison
# plot_grid(fig_2a_single_dendro,
# plot_grid(
# replicate_comp_plot + coord_fixed(ratio=1),
# interbin_volcano + coord_fixed(ratio=1),
# prolif_4v8 + coord_fixed(ratio=1),
# rel_heights=c(2.3, 2.3, 1.3),
# rel_widths=c(1,1,1),
# labels = c("B", "C", "D"), align='v', axis='lr',
# ncol = 1),
# labels=c('A',''))
bcd_add_left_margin <- theme(plot.margin = unit(c(5.5,5.5,5.5,22), "pt"))
plot_bcd <- plot_grid(
fig2_abundance_over_time + bcd_add_left_margin, #b
interbin_volcano + bcd_add_left_margin, #c
prolif_4v8_rel + bcd_add_left_margin, #d
rel_heights=c(1.6, 2.3, 1.6),
rel_widths=c(1,1,1),
labels = c("B", "C", "D"), align='v', axis='lr',
ncol = 1)
## Warning: Removed 1 rows containing missing values (geom_point).
## Warning: Removed 1 rows containing missing values (geom_text_repel).
plot_abcd <- plot_grid(
make_dendroheatmap(
chosen_metrics,
title='',
legend_theme=theme(
legend.position = c(1, -0.08),
legend.justification = c(-0.6, 0),
axis.ticks = element_blank(),
axis.text.x = element_text(angle=90, vjust = 0.5, hjust=1),
plot.margin = unit(c(5.5,38,65,5.5), "pt"))),
plot_bcd,
rel_widths=c(0.7,1),
labels=c('',''))
plot_abcde <- plot_grid(plot_abcd,
cytokine_panel +
guides(colour = guide_legend(reverse=T)) +
theme(plot.margin = unit(c(0.5,1,1,0.8), "cm")),
ncol=1,
rel_heights=c(1.1,0.3), labels=c('A','E'))
ggsave(here::here('..','figs','compiled','fig2.pdf'), plot_abcde,
height=15, width=11)
plot_abcde
## Fig 3 PCA
cast_metrics <- dcast(
total_combined, CAR.align ~ group + assay + k.type + t.type + comparison,
value.var='value.scale')
combined_pca <- prcomp(cast_metrics[,-1])
# calculate pca stats
pca.dt <- data.table(pc = data.table(colnames(combined_pca$rotation))[,
PC := as.integer(gsub("[A-Z]", "", V1))][, PC],
sd = combined_pca$sdev,
var = combined_pca$sdev^2,
var.norm = combined_pca$sdev^2/sum(combined_pca$sdev^2),
var.acc = cumsum(combined_pca$sdev^2/sum(combined_pca$sdev^2)))
melt.pca.dt <- melt(
pca.dt, measure.vars = c("var.norm", "var.acc"), variable.name = "metric")
pca.stats.plot <- ggplot(melt.pca.dt) +
geom_line(aes(x = pc, y = value, color = metric)) +
geom_point(aes(x = pc, y = value, color = metric)) +
scale_x_continuous(limits = c(1, NA)) +
labs(title = "Fraction of Variance Captured by Principal Components, Every Sort Group",
x = "Principal Component", y = "Fraction of Variance") +
theme_bw()
# project data onto principal components
projected.metrics <- scale(cast_metrics[, setdiff(names(cast_metrics),
c("CAR.align")), with = FALSE],
combined_pca$center, combined_pca$scale) %*% combined_pca$rotation
projected.metrics <- cbind.data.frame(CAR.align=cast_metrics[, CAR.align],
projected.metrics)
projected.metrics <- data.table(merge(projected.metrics,
unique(read.counts[, .(CAR.align, len)]),
by = "CAR.align"))
projected.metrics[CAR.align == '4-1BB', CAR.align := '41BB']
projected.metrics[, CAR.annot := 'none']
projected.metrics[CAR.align %in% names(car_colors), CAR.annot := CAR.align]
projected.metrics[, CAR.annot := factor(
CAR.annot, levels=c(names(car_colors)[1:7], 'none'))]
pca.pos.sort.group.plot <- ggplot(projected.metrics,
aes(x = PC1, y = PC2, label = CAR.align,
size=(CAR.align %in% names(car_colors)),
color=CAR.annot)) +
geom_point() +
geom_text_repel(data=projected.metrics[CAR.annot != 'none'],
size=4, color='grey20', box.padding = 0.5, segment.alpha=0) +
scale_size_discrete(guide=F) +
scale_color_manual('',
labels=c(names(car_colors)[1:7], ''),
values=c(car_colors[1:7], 'grey')) +
labs(title = 'PCA Using CD19+ Sort Group Bins') +
theme_bw() +
labs(title='') +
theme(axis.text = element_blank())
## Warning: Using size for a discrete variable is not advised.
ggsave(here::here('..','figs','new_pca.pdf'), pca.pos.sort.group.plot,
height=4, width=5)
pca.pos.sort.group.plot
data.output.dir <- file.path(here::here(
'..','..',
's3-roybal-tcsl',
'lenti_screen_compiled_data','data'))
save(list=ls(),
file=file.path(data.output.dir, 'pooled_deseq2_analysis_data.Rdata'))